Consistency analysis of the water cycle from recently derived satellite products
Abstract:NOAA’s National Environmental Satellite, Data, and Information Service (NESDIS) develops environmental data from satellites and other sources that is a critical resource for the management of energy, water, and food supplies. Variables related to the water cycle are routinely computed from satellite remote sensing from several space agencies, and the products are used at NOAA in operational or experimental modes. This study seeks to investigate to what extent there is consistency among the diverse products, and how they represent the water cycle at different scales.
Remote sensing of land surface temperature and radiation is used to estimate surface energy fluxes by means of the Atmosphere Land Exchange Inverse (ALEXI) model. An Evaporative Stress Index representing anomalies in the ratio of actual-to-potential is a reliable indicator of drought also obtained from the ALEXI model. Observations from all currently available microwave satellite sensors are processed and merged to obtain the best possible estimates of soil moisture. The Global Soil Moisture Operational Product System (SMOPS) may also ingest brightness temperature observations applying a single channel algorithm to retrieve soil moisture. All satellite retrievals in SMOPS are merged into a soil moisture product that includes proxies of the errors. The Global Precipitation Climatology Project (GPCP) monthly precipitation data set (a current NOAA CDR project) uses satellite precipitation data sets over ocean and satellite plus gauge-based analyses over land. For operational needs, NESDIS’s Hydro-Estimator (H-E) uses infrared data from GOES to estimate higher temporal resolution (sub-daily) rainfall rates. Streamflow at all the river mouths is estimated by the Dominant river tracing-Routing Integrated with VIC Environment model using precipitation input and other forcing data. Evapotranspiration, soil moisture, precipitation, streamflow and groundwater are derived at different resolutions, time scales and different periods. The challenge of including them into a common framework (the water cycle) will require an evaluation of each product, the assessment of their associated errors, and finding optimal ways of transferring such information into a common grid